9H10 Smac / Diablo
- Known as:
- 9H10 Smac / Diablo
- Catalog number:
- mc-280
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Kamiya biomedical company
- Gene target:
- 9H10 Smac / Diablo
Related genes to: 9H10 Smac / Diablo
- Gene:
- DIABLO NIH gene
- Name:
- diablo IAP-binding mitochondrial protein
- Previous symbol:
- -
- Synonyms:
- SMAC, DIABLO-S, FLJ25049, FLJ10537, DFNA64
- Chromosome:
- 12q24.31
- Locus Type:
- gene with protein product
- Date approved:
- 2003-10-27
- Date modifiied:
- 2018-05-03
Related products to: 9H10 Smac / Diablo
Related articles to: 9H10 Smac / Diablo
- Coronaviruses (CoVs), including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome (MERS-CoV), cause respiratory infections with distinct clinical outcomes and case fatality rates. However, the molecular basis of these differences remains unclear. In this study, we sought to define virus-specific host metabolic programs by directly comparing multiomics profiles of the lungs of lethally infected mouse models. - Source: PubMed
Publication date: 2026/08/25
Jang YeonseoKim HyeranLi YufeiLee Jae-SeungLee JaehoonLee Han-WoongCho Nam-HyukCho Joo-Youn - Atopic dermatitis (AD) is a chronic inflammatory skin disease that typically develops in early childhood. Differences in AD prevalence and allergy sensitisation patterns have been observed in African populations, including the AmaXhosa population in South Africa, suggesting alternative pathogenic and immune mechanisms underlying AD. Differences in AD prevalence have also been documented between urban and rural communities, making AmaXhosa children, who share a common ethnogenetic background but differ in environmental exposures, a unique population in which to investigate environmental and immune mechanisms underlying AD. To address this, we performed a machine learning (ML)-based multimodal observational study to identify features associated with AD in 217 AmaXhosa children. - Source: PubMed
Publication date: 2026/09/08
Zhakparov DamirLunjani NonhlanhlaSchmid MarcoMoriarty KathleenRoquero DamianDreher AnitaHeldstab-Kast Jeannette INadeau Kari CAkdis CezmiLevin MichaelHlela CarolSokolowska MilenaO'Mahony LiamBaerenfaller Katja - Alström syndrome (ALMS) and Bardet-Biedl syndrome (BBS) are rare ciliopathies characterized by multisystem involvement, including obesity, insulin resistance, and type 2 diabetes. Systemic metabolic dysfunction may influence the oral microbiome; however, integrative analyses that combine microbial and metabolic profiles in these disorders remain limited. Saliva and gingival crevicular fluid (GCF) samples were collected from genetically confirmed ALMS and BBS patients, as well as from obesity and healthy control groups. Microbial communities were profiled using V3-V4 16S rRNA gene amplicon sequencing, and untargeted metabolomic profiling was performed by gas chromatography-mass spectrometry. Microbiome-metabolome associations were evaluated using Spearman's rank correlation analysis, followed by multi-omics integration using Multiple Co-Inertia Analysis (MCIA) and the supervised Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) framework (mixOmics). Integrated analysis identified distinct microbiome-metabolome association patterns in ALMS and BBS. Compared with controls, the ALMS+BBS group showed enrichment of , , and , alongside reduced abundance. Metabolomic profiling revealed alterations in amino acid, fatty acid, and carbohydrate metabolism. GCF exhibited structured associations between metabolites and Firmicutes, Proteobacteria, and Actinobacteriota, whereas saliva showed broader interaction networks. These associations were absent or markedly weaker in obesity and healthy controls. MCIA demonstrated coordinated variation across the oral microbiome, salivary metabolome, and GCF metabolome, while DIABLO identified a shared multi-omics signature. Coordinated shifts in amino acid, lipid, and central carbon metabolism can be linked to oral microbial reorganization in ALMS and BBS. Integrative multi-omics analyses identified coordinated microbiome-metabolome signatures across the oral microbiome, saliva, and GCF. These findings warrant validation in larger longitudinal and functional studies. - Source: PubMed
Publication date: 2026/09/03
Mojsak PatrycjaZmyslowska-Polakowska EwaChmielewska SandraSołowiej KrzysztofPloszaj TomaszSkoczylas SebastianGrzybowska-Adamowicz JuliaPienkowski TomaszKretowski AdamZmyslowska AgnieszkaCiborowski Michal - Dietary inflammation may influence cardiometabolic health, yet the gut mycobiome and bacterial-fungal interactions remain unclear. Building upon our earlier findings and data from the TALENTs trial (Targeting Aging and Longevity with Exogenous Nucleotides) baseline data, we explored gut fungal profiles and bacterial-fungal co-occurrence patterns in relation to the Dietary Inflammatory Index (DII) and Life's Essential 8 (LE8) in older adults. We enrolled 301 community residents aged 60-70 years, with 285 providing qualified fungal internal transcribed spacer (ITS) sequencing data. DII scores were derived from 3-day dietary records to reflect dietary inflammatory risk, and LE8 (integrating health behaviors including physical activity and metabolic health factors including BMI, blood lipids, blood pressure, and blood glucose) was used to assess cardiovascular health; LE8_non-diet was applied as a sensitivity measure. Fungal diversity, genus-level taxa, ecological guilds, and bacterial-fungal associations were analyzed using diversity indices, ZINB/Hurdle models, bootstrap, E-values, DIABLO analysis, and network construction. DII showed an inverse correlation with LE8_non-diet (r = -0.130, = 0.024). Fungal alpha and beta diversity did not differ significantly across DII-defined groups. Conversely, better cardiovascular status was linked to higher fungal richness, with significantly elevated Chao1 and ACE indices in the high-CVH group (both < 0.05). Additionally, integrated ZINB, Hurdle, and stability analyses jointly pinpointed four candidate fungal genera that correlated with both DII and LE8. Both ZINB and DIABLO analyses consistently indicated that antagonistic interactions dominated gut bacterial-fungal associations (89.9% vs. 63.8% of negative associations, respectively), with DIABLO further revealing synchronized community-level co-variation between the two kingdoms (r = 0.325, < 0.01). FUNGuild prediction further revealed saprotrophic guilds enriched in the high-CVH group and host-associated guilds in the low-CVH group, with similar patterns across DII-defined groups. This cross-sectional study reveals gut mycobiome profiles and potential bacterial-fungal co-occurrence patterns among older adults stratified by dietary inflammatory potential and cardiovascular health status, generating testable hypotheses for subsequent nutritional and metabolic research integrating lifestyle behaviors and functional health outcomes. - Source: PubMed
Publication date: 2026/08/16
Fu YuchenWu YuxiaoWang ShuyueAn XiaoyangLi YongXu Meihong - Depression and treatment-resistant depression (TRD) are significant public health issues, but the associated network-level neurobiological mechanisms remain poorly understood. This study used magnetoencephalography (MEG) to identify altered resting-state connectivity within the default mode (DMN), executive control (ECN), salience (SN), dorsal attention (DAN), motor (MN), and visual (VN) networks as potential biomarkers of depression and treatment resistance. The study recruited 168 participants (80 healthy volunteers (HVs) and 88 currently experiencing a major depressive episode (74 with TRD and 14 without TRD (noTRD))). Data Integration Analysis for Biomarker Discovery using Latent Variable Approaches for Omics Studies (DIABLO) was used to differentiate the depression, TRD, and HV subgroups and identify neural markers of depression and treatment resistance. For differentiating the depression and HV groups, the triple network model (area under the receiver operating curve (AUROC): 0.759- 0.787)-which includes the DMN, ECN, and SN-outperformed the six-network model (AUROC: 0.747-0.762) across different bandwidths. For differentiating the TRD and HV groups, the triple network model demonstrated reasonable prediction across different bandwidths (AUROC: 0.737-0.807); potential within-network connectivity differences distinguished those with TRD from HVs, especially DMN within-network connectivity between the inferior parietal lobule and precuneus in the beta band ( <.05). Hyperconnectivity within the SN (superior parietal lobule and frontal operculum in the alpha band) and DMN (inferior parietal lobule and lateral prefrontal cortex in the beta band) was associated with number of treatment failures (ps<.05). These findings highlight key brain regions and connectivity patterns, advancing our understanding of neural mechanisms underlying depression and treatment resistance. - Source: PubMed
Publication date: 2026/08/06
Lee YoojinBallard Elizabeth DStout Jeffrey DNugent AllisonHu HiroeHurst Kelly TXu AmyZarate Carlos AGilbert Jessica R